Here are the key details for the regression competition:
Description of training data
The training dataset comprises 300 data-points with 16 dimensional inputs and one dimensional real-valued outputs. The training dataset is available as a CSV file. The first row of each CSV file contains the column names and the first column contains the data-point index (running from 1 to 300). There are standard functions to read these files in many programming languages, e.g. read.csv in R and csv.reader or numpy.loadtxt in Python.
Description of test data
The test dataset comprises 200 data-points with 16 dimensional input features. The outputs are missing and the goal is to predict them. In addition, at some of the test points a subset of the inputs is also missing. The test inputs are also available as a CSV file in the same format as the training data. Missing inputs are indicated by the value NA.
Submission of predictions
For each test datapoint you must predict the missing real-valued output. These predictions should be submitted in the same format as the training outputs. The first row must contain column names (ID, Total.Interactions). Below this row, the first column must contain the data-point index (that runs from 1 through to 200) and the second column must contain the predictions (integer numbers).